Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers K Um, R Brand, YR Fei, P Holl, N Thuerey Advances in Neural Information Processing Systems 33, 6111-6122, 2020 | 233 | 2020 |

Learning to control pdes with differentiable physics P Holl, V Koltun, N Thuerey arXiv preprint arXiv:2001.07457, 2020 | 183 | 2020 |

Physics-based deep learning N Thuerey, P Holl, M Mueller, P Schnell, F Trost, K Um arXiv preprint arXiv:2109.05237, 2021 | 118 | 2021 |

Holography of wi-fi radiation PM Holl, F Reinhard Physical review letters 118 (18), 183901, 2017 | 82 | 2017 |

phiflow: A differentiable pde solving framework for deep learning via physical simulations P Holl, V Koltun, K Um, N Thuerey NeurIPS workshop 2, 2020 | 47 | 2020 |

Deep learning based pulse shape discrimination for germanium detectors P Holl, L Hauertmann, B Majorovits, O Schulz, M Schuster, AJ Zsigmond The European Physical Journal C 79, 1-9, 2019 | 42 | 2019 |

Physics-Based Deep Learning. 2021 N Thuerey, P Holl, M Mueller, P Schnell, F Trost, K Um URL https://physicsbaseddeeplearning. org, 0 | 10 | |

Half-inverse gradients for physical deep learning P Schnell, P Holl, N Thuerey arXiv preprint arXiv:2203.10131, 2022 | 8 | 2022 |

Learning to control pdes with differentiable physics (2020) P Holl, V Koltun, N Thuerey arXiv preprint arXiv:2001.07457, 2001 | 7 | 2001 |

Simulating liquids with graph networks J Klimesch, P Holl, N Thuerey arXiv preprint arXiv:2203.07895, 2022 | 6 | 2022 |

Scale-invariant learning by physics inversion P Holl, V Koltun, N Thuerey Advances in Neural Information Processing Systems 35, 5390-5403, 2022 | 5 | 2022 |

Physical gradients for deep learning P Holl, N Thuerey, V Koltun | 2 | 2021 |

Φ-ML: Intuitive Scientific Computing with Dimension Types for Jax, PyTorch, TensorFlow & NumPy P Holl, N Thuerey Journal of Open Source Software 9 (95), 6171, 2024 | | 2024 |

: Differentiable Simulations for PyTorch, TensorFlow and Jax P Holl, N Thuerey Forty-first International Conference on Machine Learning, 0 | | |

Can Neural Networks Improve Classical Optimization of Inverse Problems? P Holl, N Thuerey | | |

Differentiable Physics for Improving the Accuracy of Iterative PDE-Solvers with Neural Networks K Um, YR Fei, P Holl, R Brand, N Thuerey | | |

Learning Time-Aware Assistance Functions for Numerical Fluid Solvers K Um, YR Fei, P Holl, N Thuerey | | |